Whisper OpenAi Tool Gradio Web implementation

Whisper is an automatic speech recognition (ASR) system Gradio Web UI Implementation

Installation

Install ffmeg on Your Device

  # on Ubuntu or Debian
  sudo apt update
  sudo apt install ffmpeg

  # on MacOS using Homebrew (https://brew.sh/)
  brew install ffmpeg

  # on Windows using Chocolatey (https://chocolatey.org/)
  choco install ffmpeg

  # on Windows using Scoop (https://scoop.sh/)
  scoop install ffmpeg

Download Program

  mkdir whisper-sppech2txt
  cd whisper-sppech2txt
  git clone https://github.com/innovatorved/whisper-openai-gradio-implementation.git .
  pip install -r requirements.txt

Run Program

  python app.py

Available models and languages (Credit)

There are five model sizes, four with English-only versions, offering speed and accuracy tradeoffs. Below are the names of the available models and their approximate memory requirements and relative speed.

Size	Parameters	English-only model	Multilingual model	Required VRAM	Relative speed
tiny	39 M	tiny.en	tiny	~1 GB	~32x
base	74 M	base.en	base	~1 GB	~16x
small	244 M	small.en	small	~2 GB	~6x
medium	769 M	medium.en	medium	~5 GB	~2x
large	1550 M	N/A	large	~10 GB	1x

For English-only applications, the .en models tend to perform better, especially for the tiny.en and base.en models. We observed that the difference becomes less significant for the small.en and medium.en models.

Screenshots

License

MIT

Reference

• https://github.com/openai/whisper
• https://openai.com/blog/whisper/

Authors

• Ved Gupta

🚀 About Me

I'm a Developer i will feel the code then write .

Support

For support, email vedgupta@protonmail.com